Welcome to BANA 4080

Brad Boehmke


  • Phonetically: “Bem” + “Key”

  • Alternatives:

    • Dr. / Professor B
    • Brad
  • Contact:

    • Read Communication Expectations Canvas page first!
    • Email: boehmkbc@ucmail.uc.edu
    • Office: Lindhall 3412




Fun Fact: Golf Obsessed


Meet Your TA


👋 Eirlys Vo

  • Senior in Business Analytics
  • Has already taken this course
  • Passionate about helping you succeed
  • Great resource for coding questions, labs, and homework

📧 Email: vopq@mail.uc.edu
🕐 Office Hours: TBD


Important

Don’t hesitate to reach out — they’re here to support you!

Today’s Agenda


  • What is data mining?
  • The challenge ahead (meet Taylor!)
  • Course overview & goals
  • AI reality check
  • Course roadmap & learning mindset
  • Tools & setup preview
  • Q&A + student discussion

What is Data Mining

Data Mining is All Around Us

Organizations use data mining to drive decisions every day.

Real-World Examples:

  • 🛒 Kroger analyzes loyalty card data to personalize digital coupons.
  • 🎶 Spotify recommends music based on your listening history and those like you.
  • 🏥 Hospitals use patient data to predict readmission risks.
  • 🏈 NFL teams analyze player movement data to improve performance and strategy.
  • 📦 Amazon tracks browsing behavior to recommend products and optimize inventory.


Important

Every time you browse, click, buy, swipe, or stream — you’re generating data.

What Is Data Mining?

The process of uncovering meaningful patterns, trends, and relationships in large data sets

Why is it important?

  • 📈 Helps organizations make better decisions
  • 🔍 Reveals insights that would otherwise go unnoticed
  • 🤖 Powers personalization, prediction, and automation
  • 💰 Drives business value in nearly every industry


Important

Data mining turns raw information into actionable knowledge

Activity

Where Do You See Data Mining?

🤔 Think about your daily routine — when are you being “mined”?

Instructions:

  1. Form groups of 2–3 students
  2. Brainstorm at least 3 examples where you think data mining is happening in your life
  3. We’ll share a few examples as a class

💬 Look for clues in:

  • Shopping & entertainment
  • Health & fitness
  • Social media & tech
  • Education or travel

Please think about this for 5 minutes.

The Challenge Ahead

Meet Taylor

Taylor is a college junior with a summer internship at a marketing analytics firm.

Day 1: The manager hands Taylor a messy spreadsheet and says:

“We’re trying to understand what drives repeat purchases. Can you dig into this and see what you find?”

Taylor freezes. 😰

Taylor has the foundation:

  • Business knowledge ✓
  • Statistical thinking ✓
  • Critical thinking ✓

What’s missing? The ability to turn raw data into actionable insights.

Important

Sound familiar? That’s what this course is about.

Challenge

Remember Taylor’s Challenge?

Now it’s your turn to experience what Taylor felt.

You’ll work with the same type of challenge Taylor faced:

You’ll get three datasets:

  • 🧾 Customer Transactions (messy!)
  • 🛒 Product Information
  • 👥 Customer Demographics

Your mission: Figure out what drives repeat purchases.

Download the data from

https://tinyurl.com/retail-data

Let’s see what you discover…

Your Turn: The Taylor Challenge

You’re now in Taylor’s shoes. What do you do next?

Group Activity: Where Would You Start?

Work in groups of 2–3 and discuss:

  • 🤔 What kinds of questions could you ask?
  • 🔍 What would you look for in the data?
  • 🛠️ What tools or skills do you wish you had?
  • 💬 What’s hard about this kind of open-ended problem?

Please think about this for 8 minutes.

Important

There’s no “right answer” — this is what real-world analysis looks like.

Debrief: What Did You Learn?

Let’s talk through what made this challenging:

  • 🧭 How did it feel to be given a vague problem?
  • 📊 What did you want to know about the data before starting?
  • 🧠 What tools or skills do you wish you had?
  • 💬 Did your group take different approaches?

Key Takeaways:

  • Real-world problems rarely come with clean instructions
  • Good data work starts with asking the right questions
  • This course will help you learn how to explore, analyze, and communicate insights from messy data

Tip

We’ll come back to this feeling at the end of the semester — you’ll be amazed at how much you’ve grown.

Course Overview

Why This Matters for YOUR Career

Data is everywhere — but insight is rare.

No matter your major, this course gives you a competitive edge:

  • 📊 Business Analytics Students → Speak the language of data science teams
  • 📈 Marketing Majors → Understand customer behavior through data, not just theory
  • 💵 Finance Majors → Model risk, detect fraud, forecast performance with code
  • 👩‍💼 Management Majors → Lead data-driven decisions instead of following them
  • 🎯 All Majors → Collaborate effectively with technical teams


Important

Today’s business leaders are expected to be data-literate decision makers

What You’ll Learn in BANA 4080

flowchart LR
  subgraph DM[Data Mining]
    direction LR
    subgraph Data
    end
    subgraph Cleaning
    end
    subgraph Wrangling
    end
    subgraph EDA
    end
    subgraph Modeling
    end
    subgraph Interpretation
    end
  end
  A[Stakeholders] --> DM
  B[Organizational Requirements] --> DM
  DM --> Decisions --> Value
  Data --> Cleaning --> Wrangling --> EDA --> Modeling --> Interpretation
  Interpretation --> Data

By the end of this course, you’ll be able to:

  • Write basic Python code to work with data
  • Clean, wrangle, and analyze messy real-world datasets
  • Visualize insights clearly and effectively
  • Understand how various ML/AI models are used in organizations
  • Build simple ML/AI models for prediction and pattern discovery
  • Communicate data-driven findings to others

Important

This course is not about memorizing syntax — it’s about thinking with data

AI Reality Check

What About AI? Won’t It Do This for Me?

“Why do I need to learn coding when ChatGPT can just do it for me?”

It’s a fair question. Let’s talk about it honestly.

🤖 AI tools are incredible accelerators, but they’re not magic:

  • They don’t understand your business context
  • They can’t ask the right questions about your data
  • They sometimes just make stuff up
  • They’re only as good as your prompts and interpretation

AI Reality Check: It’s Like Autocorrect for Code!

Warning

Ever had your phone turn “on my way!” into “omg my weasel!”? 🦫

That’s exactly how AI coding tools work — they predict what comes next based on patterns they’ve seen.

Sometimes they nail it… sometimes you get digital weasels.

AI tools are assistants, not autopilots:

AI can help you:

  • Write boilerplate code
  • Debug errors
  • Learn new syntax
  • Generate ideas

AI cannot:

  • Understand YOUR data
  • Know YOUR business goals
  • Ask the right questions
  • Guarantee correct answers

How We’ll Use AI in This Course

You’ll learn to use AI tools as learning partners, not crutches:

✅ Smart AI Use:

  • Check your understanding
  • Help debug when stuck
  • Explain concepts differently
  • Generate practice examples
  • Always understand what the code does

❌ Avoid This:

  • Copy-paste without understanding
  • Skip the learning struggle
  • Rely on AI for everything
  • Submit AI code you can’t explain

Important

The future belongs to people who know how to collaborate with AI, not be replaced by it.

Course Roadmap & Learning Mindset

Learning to Code: A Reality Check

Let’s be honest — learning to code can be frustrating at first.

You might feel:

  • 😤 Confused by error messages
  • 🤯 Like everyone else “gets it” but you
  • 😮‍💨 Stuck on simple problems
  • 🙄 Like you’re just copying examples

This is normal. It’s expected.

Learning to Code = Learning a New Language

You’ll start by copying examples and Googling errors.

Over time, you’ll stop memorizing and start thinking in code.

This course is designed for beginners — we’ll get you there step by step!

Course Roadmap

Course Roadmap

Your journey through BANA 4080:

  1. Weeks 1–3: Python basics & Working with data
  2. Weeks 3–4: Data wrangling & Exploratory analysis
  3. Weeks 5-6: Data visualization & Efficient programming
  4. Week 7: Mid-term
  5. Weeks 8-12: ML & AI
  6. Weeks 13-14: Final project

Important

This course builds your skills step-by-step — like a training plan for thinking with data.

How You’ll Learn

Each week follows a consistent rhythm:

  • 🧠 Tuesday (Lecture): Learn concepts, explore examples, discuss ideas
  • 💻 Thursday (Lab): Practice coding, get hands-on, work with real data

Assessments include:

  • 📚 Weekly reading quizzes
  • 📝 Biweekly homework assignments
  • 💭 Discussion forums
  • 📊 Midterm and final project
  • ❌ No conventional tests!

Important

Expect to build something meaningful — not just learn theory.

Resources

Everything You Need Is in One of Two Spots


📍 Course Canvas Page

📘 Course Textbook

Step 1

Who has read through the “Start Here!” module?

Let’s hit on a few important items

Tools & Setup Preview

Why Learn to Code? 🤔


  • Coding = flexibility + power
  • Handle real-world data: big, messy, inconsistent
  • Automate repetitive tasks
  • Think algorithmically and analytically

Why Python? 🤔


  • Widely used
  • Easy-to-read syntax (great for beginners)
  • Massive ecosystem: pandas, numpy, matplotlib, scikit-learn
  • Community support: tutorials, libraries, AI tools
  • Most organizations are shifting toward Python as the primary language for their data science and engineering codebases

Important

Python is the most valuable tool in your analytics toolbox.

How You’ll Run Python: Google Colab

What is Colab?

  • 💻 Free cloud-based Python environment from Google
  • 🚫 No software installation needed to get started
  • ✅ Works in your browser – just click and code

Why Colab First?

  • Easy, consistent experience for everyone on Day 1
  • Allows us to focus on learning — not debugging installs
  • We’ll gradually move toward installing tools locally (e.g., Anaconda, Jupyter, VS Code)

Important

You’ll be up and coding on Day 1 — no setup headaches!

Next Steps

Your Learning Journey Starts Now

📖 What Next?

  1. Read the “Start Here!” module
  2. Start working through Module 1’s readings - Chapters 1-3
  3. Get up and running in Colab

🗓️ Thursday Lab:

  • Your first Python code
  • Working in Google Colab
  • Collaborative problem-solving

💡 Remember: We’re building skills step-by-step!

Q&A

Q&A 🙋‍♀️

  • Open floor for any questions regarding the course structure, expectations, or content.
  • Discussion on how this course aligns with your academic and career goals.
  • Or anything else…golf?